Results 51 to 60 of about 4,356,022 (346)
Identifying the machine translation error types with the greatest impact on post-editing effort [PDF]
, 2017 Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from
Daems, Joke +3 more
core +3 more sources
Interactive Machine Translation [PDF]
, 2011 [EN] Achieving high-quality translation between any pair of languages is not possible with the current Machine-Translation (MT) technology a human post-editing of the outputs of the MT system being necessary. Therefore, MT is a suitable area to apply the Interactive Pattern Recognition (IPR) framework and this application has led to what nowadays is ...
Toselli, Alejandro Héctor +5 more
openaire +2 more sources
Translating Phrases in Neural Machine Translation [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 Accepted by EMNLP ...
Deyi Xiong +3 more
openaire +3 more sources
Comparative Legilinguistics, 2019 With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy.
Wiesmann Eva
doaj +4 more sources
Study on Post-editing for Machine Translation of Railway Engineering Texts [PDF]
SHS Web of Conferences, 2021 With rapid development of China's railways, there are more overseas construction projects and technical exchanges in the field of railway engineering, which have generated widespread demands for translation.
Li Yuting, Lu Xiuying
doaj +1 more source
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation [PDF]
Transactions of the Association for Computational Linguistics, 2016 We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token ...
Melvin Johnson +11 more
semanticscholar +1 more source
Challenges in translational machine learning [PDF]
Human Genetics, 2022 AbstractMachine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as “translational machine learning”, joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic.
Artuur Couckuyt +6 more
openaire +3 more sources
A review of existing Machine Translation Approaches, their Challenges and Evaluation Metrics
Pakistan Journal of Engineering Technology & Science, 2023 Machine translation is the process of translating a natural language into another. The primary goal of machine translation is to bridge the linguistic gap between languages.
Naseer Ahmed
doaj +1 more source
Improving Neural Machine Translation Models with Monolingual Data [PDF]
Annual Meeting of the Association for Computational Linguistics, 2015 Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical ...
Rico Sennrich +2 more
semanticscholar +1 more source
Neural Machine Translation Advised by Statistical Machine Translation
Proceedings of the AAAI Conference on Artificial Intelligence, 2017 Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces fluent but inadequate translations (Tu et al. 2016b; 2016a; He et al. 2016; Tu et al. 2017). This is in contrast to conventional Statistical Machine Translation (
Wang, Xing +5 more
openaire +2 more sources